Mini Tutorial: How to Configure a Split Test Campaign

Check out Campaign Split Testing for an in-depth look at the setup and functionality of a split-testing campaign.

Not sure which version of an offer or email subject line might perform better? Split testing takes the guesswork out by letting you compare two versions of a campaign among a divided audience. It’s a simple and effective way to uncover what truly engages your guests and make data-driven decisions for your loyalty program.

Watch this short video on how to create a split test campaign:

It’s important to maintain a consistent cycle of testing and adjusting your offers and messaging to get the most of your split testing data. Check out some more ideas for what you can use split testing for.

Offers Test Ideas

  • Variant A: 50% Off One Burger | Variant B: BOGO Burger Free
  • Variant A: Free Entree with a 3-day expiry | Variant B: Free Entree with a 7-day expiry
  • Variant A: 30% Off a $10+ Order | Variant B: $3 Off Your Next $10+ Order

Email Subject Lines Test Idea

  • Variant A: A surprise treat awaits! | Variant B: Enjoy a free side on your next visit

Push Notification Copy Test Idea

  • Variant A: Happy Birthday! Check out your reward in the app! | Variant B: It’s your big day! Unwrap your gift by going into the app and select Rewards.

Here’s some more tips and tricks:

  • Keep your eye on the prize. Stay focused on how your split testing campaign can help you make more informed decisions aligned with your overall loyalty goals. Even if an offer already converts well, dig deeper to understand whether a slightly different offer could potentially convert just as well that’s more aligned with your goal of driving dinner traffic or online orders.
  • Not all split testing needs to be 50/50. Utilize the proper segment split based on risk and confidence in your hypothesis.
  • Use AI to help come up with creative ideas for different types of offers and messaging that can be tested.
  • Utilize the “duplicate from variant A” option when performing a true A/B test where only one variable is different to help streamline your campaign configuration.